{"doi":"10.1101/2024.11.25.625273","title":"An snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging","abstract":"Abstract Although multiple high-performing epigenetic aging clocks exist, few are based directly on gene expression. Such transcriptomic aging clocks allow us to identify potential age-associated genes directly. However, most existing transcriptomic clocks model a subset of genes and are limited in their ability to predict novel biomarkers. With the growing application of single-cell sequencing, there is a need for robust single-cell transcriptomic aging clocks. Moreover, aging clocks have yet to be applied to investigate the elusive phenomenon of sex differences in aging. We introduce TimeFlies, a pan-cell-type snRNA-seq aging clock for the Drosophila melanogaster head. TimeFlies uses deep learning to classify the donor age of cells based on genome-wide gene expression profiles. Using explainability methods, we identified key marker genes contributing to the classification, with lncRNAs showing up as highly enriched among predicted biomarkers. lncRNA: roX1 and lncRNA: roX2 are top clock genes across cell types. Both are regulators of X chromosome dosage compensation, a pathway previously found to be significantly affected by aging in the mouse brain. We validated these findings experimentally in Drosophila , showing a decrease in survival when dosage compensation is inhibited in vivo . Furthermore, we trained sex-specific TimeFlies clocks and noted significant differences in model predictions and explanations between male and female clocks, suggesting that different pathways drive aging in males and females.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":489324,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9428,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1334673,"name":"Ananya G Pavuluri","orcid":null,"position":1,"is_corresponding":false},{"id":1334230,"name":"Gunjan Singh","orcid":"0000-0002-7196-5534","position":2,"is_corresponding":false},{"id":1178166,"name":"Kaitlyn Cortez","orcid":null,"position":3,"is_corresponding":false},{"id":1334674,"name":"Kate O’Connor-Giles","orcid":null,"position":4,"is_corresponding":false},{"id":23424,"name":"Erica Larschan","orcid":"0000-0003-2484-4921","position":5,"is_corresponding":false},{"id":347248,"name":"Ritambhara Singh","orcid":"0000-0002-7523-160X","position":6,"is_corresponding":false},{"id":1334672,"name":"Nikolai Tennant","orcid":null,"position":0,"is_corresponding":true}],"reference_count":57,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:08:23.929823Z","pmid":"39896546","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}